Additive Manufacturing and Quality Control for Aerospace

Additive Manufacturing (AM) is becoming increasingly important for the aerospace industry, and notably for non-destructive evaluation of components that would otherwise be costly to model. ELEMCA and the French space agency Centre National d’Etudes Spatiales (CNES) used Simpleware software to build models from X-ray CT imagery suitable for Finite Element Analysis in ANSYS of defects. This workflow was crucial to studying an aluminium AM part designed for the TARANIS satellite, comparing the image-based model with results from CAD.

The TARANIS satellite from CNES is designed to observe stormy regions above the Earth. The attitude and orbital control system (AOCS) of the TARANIS microsatellite is significant for controlling the orientation of the satellite, and uses a sun assembly sensor (SAS) to detect the sun’s position. It is therefore crucial that the support is stiff enough to maintain primary modal frequencies and performance within the dynamic environment of the rocket during the satellite’s launch.

SAS model created in Simpleware ScanIP

Simpleware software offers a quick and intuitive environment for processing 3D image data. In this case, X-ray CT scanning was used to create a set of images of the aluminium parts, including defects. Simpleware ScanIP was used by ELEMCA to segment the component and process the data to capture important elements for simulation.

Simulation of Von Mises stress in ANSYS

A Finite Element mesh was then generated using the Simpleware FE Module and exported to ANSYS Workbench 17.1 to examine Von Mises stress, with results compared to CAD simulations. The results showed good agreement, and validated the workflow for image-based simulation. The potential benefits of the method are to reduce experimental testing costs and speed up workflows by adding more virtual testing to manufacturing and design tasks.

The workflow used by ELEMCA and CNES is significant for increasing the range of options available for manufacturing critical components for aerospace and other industries. Being able to study the as-manufactured part allows for more realistic predictions of performance than using traditional experimental methods, and helps reduce the challenges of qualifying parts produced by additive manufacturing for aerospace.